Parasitism and food web dynamics of juvenile Pacific salmon
Bibliographic record
Abstract
There is an increasing realization of the diverse mechanisms by which parasites and pathogens influence the dynamics of host populations and communities. In multi‐host systems, parasites may mediate food web dynamics with unexpected outcomes for host populations. Models have been used to explore the potential consequences of interactions between hosts, parasites and predators, but connections between theory and data are rare. Here, we consider sea louse parasites (Lepeophtheirus salmonis), which directly increase mortality of juvenile salmon hosts ( Oncorhynchus spp.). We use mathematical models and field‐based experiments to investigate how the indirect effects of parasitism via predation influence mortality of sympatric juvenile chum salmon (O. keta) and pink salmon (O. gorbuscha). Our experiments show that coho salmon predators (O. kisutch) selectively prey on pink salmon and on parasitized prey. Preference for pink salmon increased slightly when prey were parasitized by sea lice, although there was considerable uncertainty regarding this result. Despite this uncertainty, we show that even the small increase in preference that we observed may be biologically significant. We calculate a critical threshold of pink salmon abundance above which chum salmon may experience a parasite‐mediated release from predation as predation shifts towards preferred prey species. This work highlights the importance of considering community interactions, such as predation, when assessing the risk that emerging parasites and pathogens pose to wildlife populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".